Update app.py

app.py

@@ -10,6 +10,9 @@ from transformers import TFAutoModelForSequenceClassification
 model = TFAutoModelForSequenceClassification.from_pretrained("ilan541/OncUponTim")
 
 def predict(your_text):
+  # split the text
+  
+  # inference
   inp = tokenizer(your_text, return_tensors='tf')
   if np.argmax(softmax(model(inp).logits)) == 0:
     return 'This content is not of high standard. It needs editing. '
@@ -17,7 +20,6 @@ def predict(your_text):
     return 'Promising content! Our algorithm predicts it will be very popular.'
 
 
-    ##### add possibility to play with the threshold
 
 iface = gr.Interface(fn=predict, inputs="text", outputs="text")
 iface.launch()